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Erschienen in:

01.01.2012

Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

verfasst von: Ozgur Kisi, Alireza Moghaddam Nia, Mohsen Ghafari Gosheh, Mohammad Reza Jamalizadeh Tajabadi, Azadeh Ahmadi

Erschienen in: Water Resources Management | Ausgabe 2/2012

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Abstract

Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.

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Titel: Intermittent Streamflow Forecasting by Using Several Data Driven Techniques
verfasst von: Ozgur Kisi
Alireza Moghaddam Nia
Mohsen Ghafari Gosheh
Mohammad Reza Jamalizadeh Tajabadi
Azadeh Ahmadi
Publikationsdatum: 01.01.2012
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 2/2012
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-011-9926-7

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